The increasing complexity of computing applications and/or workloads has resulted in increasing demand for resources used for running such applications. Applications as used herein may refer to any computing task(s) which requires certain resources in order to run (e.g. executable programs such as computational tasks, command execution, data collection, etc.). In this context, resources may encompass any types of resources that are necessary for running such applications, including hardware (such as servers, clients, mainframe computers, networks, network storage, databases, memory, central processing unit (CPU) time, scientific instruments and other computing devices), as well as software, software licenses, available network services and other non-hardware resources.
This increasing demand for resources has led to the development of computing grid systems or distributed computing environments (generally referred to herein as distributed computing environments). Generally speaking, such distributed computing environments include hardware and software infrastructure configured to form a virtual organization comprised of multiple resources, which resources may be in geographically disperse locations.
Each application or workload typically requires certain amounts and/or types of resources in order to run and complete tasks being executed by such application. The initiation of a computing application therefore generates a workload, which is handled by application workload managers. If there are sufficient available resources in the distributed computing environment, then suitable resources may be allocated to perform the application.
In a prior art distributed computing environment, the resource management functions are embedded with the applications workload management functions. This tight coupling of the resource management and applications workload management functions results in the inability of one application to be aware of the resource demands of other applications and the inability to smoothly share resources between them. (By way of example, a Java 2 Enterprise Edition (J2EE®) Application server cluster has no idea about what is going on in a batch queuing system within the same network and vice versa.) Embodiments described herein, however, decouple resource management from workload management by having a common resource manager that is used to coordinate and arbitrate access to resources among multiple workload managers.
In the prior art, the polices related to resource allocation are typically disjointed with multiple, possibly conflicting policies being specified and attached to different consuming entities (e.g. users, user groups, queues, projects, etc.). This typically results in computational complexity and possibly conflicting behaviour. In accordance with one aspect of the present invention, a unified hierarchical consumer tree allows different resource allocation policies to be set and applied at different levels within the enterprise hierarchy (e.g. at the departmental level or at the business unit level); the resource allocation policies may be applied according to the position (and the corresponding rights) of the consumer that is demanding resources to run a particular application within the consumer tree (“consumer” may refer to the individual user, a user group, a project, a department, business unit, etc. on whose behalf each application is demanded).